Abstract
Scab is one of the most important diseases in wheat. Rapid and accurate detection of wheat scab under farmland conditions is essential for timely and effectively managing the disease. This study proposes a method for automatically detecting wheat scab by using remote sensing from unmanned aerial vehicles (UAVs). In the method, contrast enhancement was carried out on acquired RGB images of wheat to highlight the diseased spots, and then an adaptively spatial feature fusion network (ASFFNet) was constructed to detect wheat scab in the images. ASFFNet used the feature enhancement module to combine the global and local features of RGB images of wheat to improve the expression ability of these features. In addition, the feature fusion module in ASFFNet adaptively fused the enhanced features at multiple scales to solve the inconsistency of features at different scales during fusion caused by too small disease areas, which improved the detection precision. The results show that the proposed method has a higher AP (average precision) than the existing object detection algorithms, single shot MultiBox detector (SSD), RetinaNet, YOLOv3 (you only look once version 3) and YOLOv4 (you only look once version 4). The proposed method can be a practical way to handle the scab detection task using UAV images. It also can provide technical references for farmland-level wheat phenotype monitoring.
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References
Abdullah-Al-Wadud, M., Kabir, M. H., Dewan, M. A. A., & Chae, O. (2007). A dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(2), 593–600. https://doi.org/10.1109/TCE.2007.381734
An, G., Xing, M., He, B., Kang, H., Shang, J., Liao, C., et al. (2021). Extraction of areas of rice false smut infection using UAV hyperspectral data. Remote Sensing, 13(16), 3185. https://doi.org/10.3390/rs13163185
Bai, G., & Shaner, G. (1994). Scab of wheat: Prospects for control. Plant Disease, 78(8), 760–766. https://doi.org/10.1094/PD-78-0760
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. https://arxiv.org/abs/2004.10934
Dweba, C., Figlan, S., Shimelis, H., Motaung, T., Sydenham, S., Mwadzingeni, L., et al. (2017). Fusarium head blight of wheat: Pathogenesis and control strategies. Crop Protection, 91, 114–122. https://doi.org/10.1016/j.cropro.2016.10.002
Francesconi, S., Harfouche, A., Maesano, M., & Balestra, G. M. (2021). UAV-based thermal, RGB imaging and gene expression analysis allowed detection of Fusarium head blight and gave new insights into the physiological responses to the disease in durum wheat. Frontiers in Plant Science, 12, 628575. https://doi.org/10.3389/fpls.2021.628575
Guo, A., Huang, W., Dong, Y., Ye, H., Ma, H., Liu, B., et al. (2021). Wheat yellow rust detection using UAV-based hyperspectral technology. Remote Sensing, 13(1), 123. https://doi.org/10.3390/rs13010123
He, K., Sun, J., & Tang, X. (2010). Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2341–2353. https://doi.org/10.1109/TPAMI.2010.168
He, M.-X., Hao, P., & Xin, Y.-Z. (2020). A robust method for wheatear detection using UAV in natural scenes. IEEE Access, 8, 189043–189053. https://doi.org/10.1109/ACCESS.2020.3031896
Hong, Q., Jiang, L., Zhang, Z., Ji, S., Gu, C., Mao, W., et al. (2022). A lightweight model for wheat ear fusarium head blight detection based on RGB images. Remote Sensing, 14(14), 3481. https://doi.org/10.3390/rs14143481
Huang, L., Wu, K., Huang, W., Dong, Y., Ma, H., Liu, Y., et al. (2021). Detection of fusarium head blight in wheat ears using continuous wavelet analysis and PSO-SVM. Agriculture, 11(10), 998. https://doi.org/10.3390/agriculture11100998
Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019). Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access, 7, 59069–59080. https://doi.org/10.1109/ACCESS.2019.2914929
Kwak, G.-H., & Park, N.-W. (2019). Impact of texture information on crop classification with machine learning and UAV images. Applied Sciences, 9(4), 643. https://doi.org/10.3390/app9040643
Lin, M., Corsi, B., Ficke, A., Tan, K.-C., Cockram, J., & Lillemo, M. (2020). Genetic mapping using a wheat multi-founder population reveals a locus on chromosome 2A controlling resistance to both leaf and glume blotch caused by the necrotrophic fungal pathogen Parastagonospora nodorum. Theoretical and Applied Genetics, 133(3), 785–808. https://doi.org/10.1007/s00122-019-03507-w
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980–2988). IEEE.
Liu, L., Dong, Y., Huang, W., Du, X., Ren, B., Huang, L., et al. (2020). A disease index for efficiently detecting wheat fusarium head blight using sentinel-2 multispectral imagery. IEEE Access, 8, 52181–52191. https://doi.org/10.1109/ACCESS.2020.2980310
Liu, S., Huang, D., & Wang, Y. (2019). Learning spatial fusion for single-shot object detection. https://arxiv.org/abs/1911.09516
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., et al. (2016). SSD: Single Shot MultiBox Detector. In 14th European Conference on Computer Vision (ECCV) (pp. 21–37). Springer.
Ma, H., Huang, W., Dong, Y., Liu, L., & Guo, A. (2021). Using UAV-based hyperspectral imagery to detect winter wheat fusarium head blight. Remote Sensing, 13(15), 3024. https://doi.org/10.3390/rs13153024
Marin, D. B., Ferraz, G., Santana, L. S., Barbosa, B. D. S., Barata, R. A. P., Osco, L. P., et al. (2021). Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models. Computers and Electronics in Agriculture, 190, 106476. https://doi.org/10.1016/j.compag.2021.106476
Nevavuori, P., Narra, N., & Lipping, T. (2019). Crop yield prediction with deep convolutional neural networks. Computers and Electronics in Agriculture, 163, 104859. https://doi.org/10.1016/j.compag.2019.104859
Qiu, R., Yang, C., Moghimi, A., Zhang, M., Steffenson, B. J., & Hirsch, C. D. (2019). Detection of fusarium head blight in wheat using a deep neural network and color imaging. Remote Sensing, 11(22), 2658. https://doi.org/10.3390/rs11222658
Rahman, Z.-U., Jobson, D. J., & Woodell, G. A. (2004). Retinex processing for automatic image enhancement. Journal of Electronic Imaging, 13(1), 100–110. https://doi.org/10.1117/1.1636183
Rangarajan, A. K., Whetton, R. L., & Mouazen, A. M. (2022). Detection of fusarium head blight in wheat using hyperspectral data and deep learning. Expert Systems with Applications, 208, 118240. https://doi.org/10.1016/j.eswa.2022.118240
Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. https://arxiv.org/abs/1804.02767
Ren, S., Guo, B., Wu, X., Zhang, L., Ji, M., & Wang, J. (2021). Winter wheat planted area monitoring and yield modeling using MODIS data in the Huang-Huai-Hai Plain, China. Computers and Electronics in Agriculture, 182, 106049. https://doi.org/10.1016/j.compag.2021.106049
Ultralytics. (2020). YOLOv5. Retrieved August 06, 2022, from https://github.com/ultralytics/yolov5
Wang, H., Chen, D., Li, C., Tian, N., Zhang, J., Xu, J.-R., et al. (2019). Stage-specific functional relationships between Tub1 and Tub2 beta-tubulins in the wheat scab fungus Fusarium graminearum. Fungal Genetics and Biology, 132, 103251. https://doi.org/10.1016/j.fgb.2019.103251
Wang, C.-Y., Liao, H.-Y. M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., & Yeh, I.-H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 390–391). IEEE.
Xiao, Y., Dong, Y., Huang, W., Liu, L., & Ma, H. (2021). Wheat fusarium head blight detection using UAV-based spectral and texture features in optimal window size. Remote Sensing, 13(13), 2437. https://doi.org/10.3390/rs13132437
Xu, W. C., Lan, Y. B., Li, Y. H., Luo, Y. F., & He, Z. Y. (2019). Classification method of cultivated land based on UAV visible light remote sensing. International Journal of Agricultural and Biological Engineering, 12(3), 103–109. https://doi.org/10.25165/j.ijabe.20191203.4754
Zhang, P., Ji, H., Wang, H., Liu, Y., Zhang, X., & Ren, C. (2021). Quantitative evaluation of impact damage to apples using NIR hyperspectral imaging. International Journal of Food Properties, 24(1), 457–470. https://doi.org/10.1080/10942912.2021.1900240
Zhang, D.-Y., Luo, H.-S., Wang, D.-Y., Zhou, X.-G., Li, W.-F., Gu, C.-Y., et al. (2022). Assessment of the levels of damage caused by Fusarium head blight in wheat using an improved YoloV5 method. Computers and Electronics in Agriculture, 198, 107086. https://doi.org/10.1016/j.compag.2022.107086
Zhao, J., Yan, J., Xue, T., Wang, S., Qiu, X., Yao, X., et al. (2022). A deep learning method for oriented and small wheat spike detection (OSWSDet) in UAV images. Computers and Electronics in Agriculture, 198, 107087. https://doi.org/10.1016/j.compag.2022.107087
Zhao, J., Zhang, X., Yan, J., Qiu, X., Yao, X., Tian, Y., et al. (2021). A wheat spike detection method in UAV images based on improved YOLOv5. Remote Sensing, 13(16), 3095. https://doi.org/10.3390/rs13163095
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2020). Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence (pp. 12993–13000). AAAI.
Zhou, G., Zhang, W., Chen, A., He, M., & Ma, X. (2019). Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion. IEEE Access, 7, 143190–143206. https://doi.org/10.1109/ACCESS.2019.2943454
Zhu, J., Yang, G., Feng, X., Li, X., Fang, H., Zhang, J., et al. (2022). Detecting wheat heads from UAV low-altitude remote sensing images using deep learning based on transformer. Remote Sensing, 14(20), 5141. https://doi.org/10.3390/rs14205141
Funding
The work was funded by the Anhui Natural Science Foundation (Grant No. 2208085MC60), the National Natural Science Foundation of China (Grant No. 62273001), the Science and Technology Plan Project of Inner Mongolia Autonomous Region (Grant No. 2022YFSJ0039), the Key Research and Technology Development Projects of Anhui Province (Grant No. 202004a06020045), the Scientific Research Project of Anhui Universities Graduate (Grant No.YJS20210013).
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Bao, W., Liu, W., Yang, X. et al. Adaptively spatial feature fusion network: an improved UAV detection method for wheat scab. Precision Agric 24, 1154–1180 (2023). https://doi.org/10.1007/s11119-023-10004-0
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DOI: https://doi.org/10.1007/s11119-023-10004-0